Method and system for characterizing the movement speed of particles contained in a liquid, such as blood particles

ABSTRACT

The method according to the invention is capable of characterizing a variation in the speed of particles or agglomeration of particles, the particles, such as blood particles, being contained in a liquid ( 12 ). 
     The characterization method includes the following steps:
         introducing the liquid ( 12 ) into a fluid chamber ( 14 );   lighting the fluid chamber ( 14 ) using an excitation light beam ( 18 ) emitted by a light source ( 16 ), the light beam ( 18 ) extending through the fluid chamber ( 14 ) in a longitudinal direction (X);   acquiring at least one image using a matrix photodetector ( 20 ), the image being formed by radiation transmitted by the lighted fluid chamber ( 14 ); and   calculating, from at least one acquired image, at least one indicator characterizing the variation of the speed or agglomeration of the particles.       

     During the acquisition step, the photodetector ( 20 ) is positioned at a distance (D 2 ) smaller than 1 cm from the fluid chamber ( 14 ) in the longitudinal direction (X).

The present invention relates to a method for characterizing a variation in the speed of particles or agglomeration of particles, the particles, such as blood particles, being contained in a liquid, the method including the following steps:

-   -   introducing the liquid into a fluid chamber;     -   lighting the fluid chamber using an excitation light beam         emitted by a light source, the light beam extending through the         fluid chamber in a longitudinal direction;     -   acquiring at least one image using a matrix photodetector, the         image being formed by radiation transmitted by the lighted fluid         chamber; and     -   calculating, from at least one acquired image, at least one         indicator characterizing the variation of the speed or         agglomeration of the particles.

The invention also relates to a system for characterizing the variation of the speed of particles or agglomeration of particles contained in the liquid, for example blood particles.

The invention in particular relates to the field of lenseless imaging of the light beam lighting the fluid chamber, in order to characterize a liquid, such as blood.

The invention in particular applies to the determination of a parameter concerning the coagulation of blood, in particular the measurement of the coagulation time. It also applies to the determination of a parameter regarding the agglutination of particles in the blood, in particular the determination of the blood group by characterizing a cell aggregation between the blood to be tested and an antibody.

Known from document EP 2,233,923 A1 is a characterization method and system of the aforementioned type. The described method aims to characterize the coagulation or sedimentation dynamics of a fluid containing blood. The system for implementing this method comprises a fluid chamber receiving liquid, a spatially coherent light source capable of emitting a lighting light beam and a mirror for reflecting the light beam toward the chamber. The light beam extends in a longitudinal direction from the reflecting mirror toward the fluid chamber.

The system also comprises an image sensor, such as a matrix sensor of the CCD (Charged-Coupled Device) or CMOS (Complementary Metal Oxide Semiconductor) type, arranged to make it possible to acquire a temporal series of images of an optical granularity pattern created by the interaction between the particles contained in the chamber and the light beam. The characterization system also comprises a processing unit for processing said temporal series of images.

The fluid chamber is positioned between the mirror and the image sensor in the longitudinal direction. The distance between the fluid chamber and the image sensor in the longitudinal direction is several centimeters or tens of centimeters. The light beam emitted by the spatially coherent light source has a surface comprised between 10 μm² and several mm² along a plane perpendicular to the longitudinal direction and passing through the fluid chamber.

Such a system and method make it possible to effectively characterize the coagulation or sedimentation dynamics of the blood contained in the liquid.

However, such a system is relatively bulky. Furthermore, it makes it possible to observe the coagulation phenomenon only in a relatively small volume of the fluid chamber.

The aim of the invention is therefore to propose a characterization method and system making it possible to observe a larger volume of liquid while limiting the bulk of the characterization system.

To that end, the invention relates to a characterization method of the aforementioned type, characterized in that, during the acquisition step, the photodetector is positioned at a distance smaller than 1 cm from the fluid chamber in the longitudinal direction.

According to other advantageous aspects of the invention, the characterization method comprises one or more of the following features, considered alone or according to any technically possible combinations:

-   -   the light beam has a surface area comprised between 5 mm² and         200 mm², preferably equal to 25 mm², in a plane perpendicular to         the longitudinal direction, said plane being arranged in contact         with the fluid chamber;     -   the light beam directly lights the fluid chamber, and the image         is formed directly by the radiation transmitted by the lighted         fluid chamber, in the absence of a magnifying lens positioned         between the fluid chamber and the photodetector; this does not         rule out the possible use of objective microlenses positioned at         each pixel of the photodetector;     -   during the acquisition step, several transmission images are         acquired sequentially at different moments, and during the         calculation step, a first calculated indicator is a correlation         indicator able to characterize the variation of the speed of the         particles, the correlation indicator being representative of the         correlation between at least two transmission images,         respectively acquired at moments n and n+m, or the correlation         for a predetermined region of said transmission images;     -   the method also includes a step for mixing the liquid with a         reagent capable of favoring slowing of the particles, such as a         reagent capable of favoring slowing of the blood particles by         means of coagulation of the blood;     -   the method also includes a step for determining, from the first         calculated indicator of the coagulation of blood particles         and/or from a time interval, called coagulation time, between an         initial moment and the moment at which the first calculated         indicator takes a predetermined value;     -   the light source is a spatially and temporally coherent light         source, such as a laser;     -   the first indicator is calculated from a correlation image         defining the spatial correlation between said transmission         images, or from a predetermined region of said correlation         image;     -   the correlation image is determined by the following equation:

${{Icorr}_{n + m}\left( {x,y} \right)} = \frac{\left( {{\left( {A_{n} \times A_{n + m}} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}{\sqrt{\left( {{\left( A_{n}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}\sqrt{\left( {{\left( A_{n + m}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}}$

where x and y represent the coordinates of a point of the image, Icorr_(n+m)(x,y) is a matrix having X rows and Y columns,

k1(x,y) represents a predetermined matrix having P rows and Q columns,

A_(n)(x,y) and A_(n+1)(x,y) are defined by the following equations: A _(n)(x,y)=I _(n)(x,y)−(I _(n) {circle around (x)}k1)(x,y) A _(n)(x,y)=I _(n+m)(x,y)−(I _(n+m) {circle around (x)}k1)(x,y)

I_(n)(x,y), I_(n+m)(x,y) representing two successive transmission images at moments n and n+m, I_(n)(x,y), I_(n+m)(x,y) being matrices with X rows and Y columns,

and the symbol {circle around (x)} represents the convolution defined by:

${\left( {{F \otimes k}\; 1} \right)\left( {x,y} \right)} = {\sum\limits_{p = 0}^{P}\;{\sum\limits_{q = 0}^{Q}\;{{F\left( {{x - p},{y - q}} \right)}k\; 1\left( {p,q} \right)}}}$

F(x,y) being a matrix with X rows and Y columns,

X, Y, P and Q being integers verifying X≧P≧1 and Y≧Q≧1;

-   -   the first indicator is calculated using the following equation:

${{Ind}\; 1_{n}},_{n + m}{= \frac{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{\sqrt{C_{n}^{2}\left( {x,y} \right)}\sqrt{C_{n + m}^{2}\left( {x,y} \right)}}}{\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{C_{n}^{2}\left( {x,y} \right)}}\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{C_{n + m}^{2}\left( {x,y} \right)}}}}$

where Ind1_(n,n+m) represents the first indicator,

C_(n)(x,y) and C_(n+m)(x,y) are defined by the following equations: C _(n)(x,y)=I′ _(n)(x,y)− I′ _(n) C _(n+m)(x,y)=I′ _(n+m)(x,y)− I′ _(n+m)

I′_(n)(x,y), I′_(n+m)(x,y) respectively represent a predetermined region of two successive transmission images at moments n and n+m, x and y designating the coordinates of a point of the image, I′_(n)(x,y), I′_(n+m)(x,y) being matrices having N rows and M columns, and

I′_(n) , I′_(n+m) representing a mean value of respective predetermined regions I′_(n)(x,y) and I′_(n+m)(x,y);

-   -   the method also includes a step for mixing the liquid with a         reagent capable of creating an agglomeration of particles, in         which, during the calculation step, a second calculated         indicator is an indicator for each acquired image, the second         indicator being representative of the intensity of the pixels of         the image in a predetermined region of the image and wherein the         method also includes a step for determining an agglomeration         state of the particles from the second calculated indicator;     -   the agglomeration state is determined when the second indicator         exceeds a predetermined threshold;     -   the agglomeration state is determined when the second indicator         exceeds a reference indicator, obtained by an image made in the         reference area;     -   the blood particles are red blood cells, the reagent includes an         antibody, and a piece of information relative to the blood group         is also determined from the agglomeration state;     -   the liquid includes an analyte, the method then including         estimating the quantity of said analyte in the liquid, depending         on said second indicator; and     -   the fluid chamber includes several fluid circulation channels,         and wherein, during the calculation step, an indicator is         calculated for each of the channels.

The invention also relates to a system for characterizing the variation of the speed of particles or the agglomeration of particles, the particles, such as blood particles, being contained in the liquid, the system comprising:

-   -   a fluid chamber designed to receive the liquid;     -   a light source capable of emitting an excitation light beam to         light the fluid chamber, the light beam extending through the         fluid chamber in a longitudinal direction;     -   a matrix photodetector capable of acquiring at least one image,         of a radiation transmitted by the lighted fluid chamber; and     -   an information processing unit including calculation means for         calculating, from at least one acquired image, at least one         indicator characterizing the variation of the speed or the         agglomeration of the particles;

characterized in that the photodetector is positioned at a distance smaller than 1 cm from the fluid chamber in the longitudinal direction.

According to another advantageous aspect of the invention, the matrix photodetector includes a plurality of pixels, each pixel having dimensions each smaller than or equal to 4 μm.

These features and advantages of the invention will appear upon reading the following description, provided solely as a non-limiting example, and done in reference to the appended drawings, in which:

FIG. 1 is a very diagrammatic illustration of a characterization system according to the invention, comprising a fluid chamber receiving liquid to be characterized, a light source capable of lighting the chamber in a longitudinal direction, a matrix photodetector for acquiring images of the radiation transmitted by the lighted chamber and an information processing unit,

FIG. 2 is a very diagrammatic illustration of the characterization system according to the invention, according to another arrangement of the light source relative to the matrix photodetector,

FIG. 3 is a diagrammatic view of the fluid chamber in the longitudinal direction, as well as the arrangement of the matrix photodetector of FIG. 1 relative to the chamber, according to a first alternative,

FIG. 4 is a view similar to that of FIG. 3 according to a second alternative,

FIG. 5 is a flowchart of a characterization method according to the invention,

FIG. 6 is an image of an empty channel of the chamber of FIG. 1, acquired by the matrix photodetector,

FIGS. 7 and 8 are images of the chamber containing the liquid, acquired by the photodetector at different moments,

FIGS. 9 and 10 are correlation images, calculated by the processing unit of FIG. 1, from images acquired at different moments,

FIG. 11 is a depiction of the evolution over time of an indicator characterizing a variation of the speed of the particles contained in the liquid, such as a slowing of the particles,

FIG. 12 is a table illustrating the case of cell aggregations as a function of the blood group and the antibody deposited,

FIG. 13 is a view similar to that of FIGS. 3 and 4 according to a second embodiment, the fluid chamber including two channels,

FIGS. 14 and 15 are respective images of the first and second channels of the chamber of FIG. 13, the second channel having a cell aggregation, the images being acquired by the photodetector of FIG. 1,

FIGS. 16 and 17 are histograms of the gray level of the images acquired in FIGS. 14 and 15,

FIGS. 18 to 21 are images of the liquid to be characterized, acquired by the photodetector according to a second example of the second embodiment, the liquid to be characterized containing blood, to which a variable quantity of antibodies is added, these images being acquired for increasing quantities of antibodies,

FIGS. 22 to 25 are histograms of the gray level of the images acquired in FIGS. 18 to 21, respectively,

FIGS. 26 to 29 are images of the liquid characterized in FIGS. 18 to 21, respectively, obtained, after dilution, using a microscope and forming reference images,

FIGS. 30 to 34 are images of the liquid to be characterized, acquired by the photodetector according to a third example of the second embodiment, the liquid to be characterized containing blood, a variable quantity of A protein, and a same added quantity of antibodies,

FIGS. 35 to 39 are histograms of the gray level of the images acquired in FIGS. 30 to 34, respectively,

FIGS. 40 to 44 are images of the liquid characterized in FIGS. 30 to 34, respectively, obtained after dilution using a microscope and forming reference images, and

FIG. 45 is a very diagrammatic illustration of the agglutination of the red blood cells and A proteins using antibodies.

In FIG. 1, a characterization system 10 is designed to characterize a variation of the speed of particles or the agglomeration of particles, the particles, such as blood particles, being contained in a liquid 12, through the acquisition of images formed by a radiation transmitted by the lighted liquid 12, then processing those images. The variation of the speed of particles is, for example, a slowing of the particles as will be described in more detail hereafter. One skilled in the art will understand that the characterization system 10 according to the invention is similarly capable of characterizing the acceleration of the particles.

Thus, in general, the characterization system 10 is designed to characterize a parameter of a liquid comprising particles, that liquid in particular being blood. This parameter is, for example, a coagulation or an agglomeration of particles making up the liquid. Alternatively, it is a count of the particles or an observation of the morphology of the particles.

The term “particles” in particular refers to a biological particle, i.e., a cell (for example, a red blood cell, a white blood cell, or a platelet), a bacteria, a virus, or any other molecule (for example, a protein).

Agglutination (or agglomeration) refers to the formation of a three-dimensional structure of particles connected to each other, under the effect of a reagent that has been introduced.

Agglutination (or agglomeration) state refers to an estimate, which may be relative or absolute, of the size of the agglutinates or relative to the quantity of particles present in the agglutinates.

The characterization system 10 comprises a fluid chamber 14 designed to receive the liquid 12, a light source 16 capable of emitting an excitation light beam 18 to light the fluid chamber 14, the light beam 18 oriented in a longitudinal direction X through the fluid chamber 14, and a matrix photodetector 20 capable of acquiring images of the radiation transmitted by the fluid chamber 14 lighted by the light beam 18. Transmitted radiation refers to the radiation passing through the fluid chamber, such that the matrix photodetector 20 and the light source 16 are situated on either side of the fluid chamber 14.

The characterization system 10 comprises an information processing unit 21 and a screen 22 for displaying an image of the chamber 14.

In the described embodiment, the characterization system 10 is capable of characterizing the coagulation of the blood or the agglutination of blood particles, the agglutination of blood particles making it possible to determine the associated blood group. The liquid 12 then contains blood. The liquid 12 is, for example, whole blood, a fraction of the blood, or a blood plasma. Alternatively, the liquid 12 is another bodily fluid, such as urine, perspiration, etc.

The fluid chamber 14 is positioned between the light source 16 and the matrix photodetector 20 in the longitudinal direction X. The fluid chamber 14 comprises a deposition area 26 of the liquid and one or more circulation channels 28 for the liquid 12, as shown in FIG. 3.

The fluid chamber 14 includes at least one fluid channel, delimited, in direction X, by an upper plate and a lower plate, not shown. These plates are at least partially translucent so as to make it possible to light the liquid 12 using the light source 16, as well as to detect the radiation transmitted by the matrix detector 20.

The lower and upper plates are, for example, glass slides, not shown, and separated by spacers, not shown, such that the glass slides are separated by approximately 160 μm in the longitudinal direction X.

The fluid chamber 14 has a thickness E in the longitudinal direction X. The thickness E for example has a value comprised between 20 μm and 1000 μm, preferably comprised between 30 μm and 300 μm.

The light source 16 is capable of emitting the light beam 18 in the longitudinal direction X.

The light source 16 is positioned at a first distance D1 from the fluid chamber 14 in the longitudinal direction X. The first distance D1 preferably has a value comprised between 1 cm and 30 cm, for example equal to 20 cm.

In the described embodiment, the light source 16 is a spatially and temporally coherent source. The light source 16 is, for example, a laser. Alternatively, the light source 16 is a laser diode (LD) or a laser diode of the VCSEL (Vertical Cavity Surface Emitting Laser) type.

Also alternatively, the light source 16 is a light-emitting diode (LED), monochromatic and having small enough dimensions to be considered spatially coherent, the diameter of the LED being smaller than 1/10 of the first distance D1 separating that LED from the chamber.

The light beam 18, oriented in the longitudinal direction X, has, at the level of the fluid chamber, a surface area comprised between 5 mm² and 200 mm², preferably equal to 25 mm², in a plane P perpendicular to the longitudinal direction X, as shown in FIG. 1. The plane P is arranged in contact with the fluid chamber 14. Thus, the lighted fluid surface is larger than in the state of the art. This makes it possible to eliminate local fluctuations of the parameter that one wishes to determine.

The light beam 18 is capable of lighting the fluid chamber 14 directly, preferably in the absence of a magnification lens positioned between the light source 16 and the fluid chamber 14.

The matrix photodetector 20 is a pixelated image sensor, including a plurality of pixels, not shown. Each pixel of the photodetector 20 has dimensions smaller than or equal to 10 μm, or even 4 μm. Each pixel is, for example, square, each side having a value smaller than or equal to 10 μm, or even 4 μm. In the described embodiment, each pixel is in the form of a square with sides measuring 4 μm. Alternatively, each pixel is in the form of a square with each side measuring 2.2 μm.

The matrix photodetector 20 is positioned at a second distance D2 from the fluid chamber 14 in the longitudinal direction X. The second distance D2 has a value smaller than 1 cm, and preferably comprised between 100 μm and 2 mm. Favoring a short distance between the detector and the chamber makes it possible to limit the interference phenomena between the different diffraction patterns. In fact, when this distance increases, these interferences can make the image unusable, in particular when the number of diffracting particles increases. This is due to the fact that the volume of fluid that is lighted is greater than in the device described in application EP 2,233,923 A1 of the state of the art. By placing the detector at a distance of more than 1 cm away, the image obtained on the detector would be difficult to use.

The images acquired by the matrix photodetector 20 are formed by the radiation transmitted directly by the lighted fluid chamber 14, in the absence of a magnification lens positioned between the fluid chamber 14 and the matrix photodetector 20. The matrix photodetector 20 is also called a lenseless imaging device, and is capable of forming an image of the fluid chamber 14 while being placed at a small distance therefrom. A small distance refers to a distance smaller than 1 cm.

The matrix photodetector 20 is capable of generating at least one image every 5 seconds, and the acquisition rhythm is therefore greater than 0.2 Hz. The matrix photodetector 20 is a two-dimensional image sensor, i.e., in a plane perpendicular to the longitudinal axis X. The acquisition frequency of the images is preferably comprised between 1 Hz and 20 Hz.

The matrix photodetector 20 is for example a CCD sensor. Alternatively, the photodetector 20 is a CMOS sensor.

The matrix photodetector 20 is for example substantially aligned with the fluid chamber 14 in the longitudinal direction X, as illustrated in FIG. 3, where the photodetector 20 is shown in dotted lines.

Alternatively, the matrix photodetector 20 is slightly offset relative to the chamber 14 along the longitudinal axis X, as illustrated in FIG. 4, where the photodetector 20 is also shown in dotted lines.

The information processing unit 21, shown in FIG. 1, includes a data processor 30 and a memory 32 associated with the processor.

In the example embodiment of FIG. 2, the matrix photodetector 20, the light source 16, and optionally all or part of the information processing unit 21 are secured to a second substrate 23. The characterization system 10 comprises an optical system 24, for example a mirror, making it possible to return the light beam 18 from the light source 16 toward the photodetector 20. This makes it possible to have a compact system. The fluid chamber 14 is for example formed in a removable support 25. The removable support 25 is for example disposable and designed to be inserted overhanging the photodetector 20, at a small distance therefrom, such that the fluid chamber can be lighted by the light beam 18. According to this example embodiment, the support 25 is designed to receive the fluid to be analyzed 12, then to be inserted near the photodetector 20 so that the analysis can be done. It for example includes a conduit, in which the fluid 12 circulates as far as the channel 28 of the fluid chamber, the fluid chamber 14 being connected to that conduit. When the analysis is complete, the support 25 is removed, in particular to be thrown away. The characterization system 10 is then available to perform another measurement with another support.

One skilled in the art will understand that, in the example embodiment of FIG. 2, the longitudinal direction X corresponds to the last portion of the light beam 18 between the corresponding mirror of the optical system 24 and the photodetector 20, passing through the fluid chamber 14.

The or each circulation channel 28 has a width L, shown in FIGS. 3 and 4. The width L for example has a value comprised between 50 μm and 5 mm, preferably equal to 1.5 mm.

The memory 32 is capable of storing software 34 for receiving the images acquired by the matrix photodetector 20, first software 36 for calculating a first indicator Ind1_(n,n+m) capable of characterizing the desired parameter; in this case, the variation of the speed of the particles, such as their slowing. Additionally or alternatively, the memory 32 can store second software 38 for calculating a second indicator Ind2 capable of characterizing another desired parameter, in this case the agglomeration of the particles. The memory 32 is also capable of storing software 40 for characterizing the variation of the speed of the particles and/or the agglomeration of the particles.

Alternatively, the reception means 34, the first calculation means 36, the second calculation means 38 and the characterization means 40 are made in the form of programmable logic components or in the form of dedicated integrated circuits.

The reception software 34 is capable of regularly receiving, from the photodetector 20, the images acquired sequentially at different moments. The reception software 34 is capable of receiving at least one image per second, and the reception rhythm of the images is greater than 0.2 Hz, typically from 1 Hz to 20 Hz.

The first calculation software 36 is capable of calculating an image A_(n), representing the transmission image I_(n)(x,y), from which a local mean is taken out. The latter is obtained by convoluting the image I_(n)(x,y) with a kernel k1. This kernel k1 is a matrix with small dimensions relative to I_(n). For example, the dimensions of the kernel k1 are 10 pixels by 10 pixels, and the dimensions of I_(n) are at least twice as large as those of the kernel k1, or even 10 times larger. The kernel k1, including P rows and Q columns, is for example homogenous, all of its values being identical. According to the preceding, P and Q are integers, for example equal to 10. Thus, two images A_(n) and A_(n+m) are established, respectively corresponding to the moments n and n+m, m being an integer. In general, m is equal to 1, the transmission images I_(n) and I_(n+1) being two successive transmission images. A _(n)(x,y)=I _(n)(x,y)−(I _(n) {circle around (x)}k1)(x,y)  (1) A _(n+m)(x,y)=I _(n+m)(x,y)−(I _(n+m) {circle around (x)}k1)(x,y)  (2)

where I_(n)(x, y), I_(n+m)(x, y) represent two successive transmission images at moments n and n+m, x and y representing the coordinates of a point of the respective image, I_(n)(x,y), I_(n+m)(x,y) being matrices having X rows and Y columns, the symbol {circle around (x)} representing the convolution defined by the following equation:

$\begin{matrix} {{\left( {{F \otimes k}\; 1} \right)\left( {x,y} \right)} = {\sum\limits_{p = 0}^{P}\;{\sum\limits_{q = 0}^{Q}\;{{F\left( {{x - p},{y - q}} \right)}k\; 1\left( {p,q} \right)}}}} & (3) \end{matrix}$

F being a matrix with X rows and Y columns,

k1 representing a kernel for the correlation of the acquired images, k1 being a matrix with P rows and Q columns,

X, Y, P and Q being integers verifying X≧P≧1 and Y≧Q≧1.

The images are for example acquired every second by the matrix photodetector 20, and the two transmission images I_(n)(x,y), I_(n+1)(x,y) are then images acquired with an interval of one second.

The first calculation software 36 is then capable of calculating a correlation image Icorr_(n,n+m)(x,y) representative of the correlation between two transmission images I_(n)(x,y), I_(n+m)(x,y) for example according to the following equation:

$\begin{matrix} {{{Icorr}_{n,{n + m}}\left( {x,y} \right)} = \frac{\left( {{\left( {A_{n} \times A_{n + m}} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}{\sqrt{\left( {{\left( A_{n}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}\sqrt{\left( {{\left( A_{n + m}^{2} \right) \otimes k}\; 1} \right)\left( {x,y} \right)}}} & (4) \end{matrix}$

where Icorr_(n,n+m)(x,y) represents the correlation image of two transmission images I_(n), I_(n+m), established at respective moments n and n+m; x and y represent the coordinates of a point of the image, Icorr_(n,n+m)(x,y) being a matrix with X rows and Y columns.

The first calculation software 36 is lastly capable of calculating the first indicator Ind1_(n,n+m) from the correlation image Icorr_(n,n+m)(x,y) previously obtained. This indicator Ind1_(n,n+m) is representative of the intensity of the image Icorr_(n,n+m)(x,y). This indicator Ind1_(n,n+m) is then capable of characterizing the variation of the speed of the particles, such as their slowing.

The correlation indicator Ind1_(n,n+m) is representative of the correlation between at least two transmission images I_(n)(x,y) and I_(n+m)(x,y) respectively acquired at moments n and n+m, that correlation being established for a region of interest 142 of the correlation image Icorr_(n,n+m)(x,y). Said region of interest 142 is determined by the user. It corresponds to the area of the correlation image Icorr_(n,n+m)(x,y) that one wishes to use to determine the correlation indicator Ind1_(n,n+m). It is for example a square area having several dozen pixels per side, for example 50×50 pixels. The correlation indicator Ind1_(n,n+m) translates the value of the intensity in that region of interest 142. It is in particular determined from the mean intensity or the total intensity in the region of interest 142 of the image Icorr_(n,n+m)(x,y). That indicator Ind1_(n,n+m) for example represents the mean intensity level or said total intensity in the region of interest 142.

Alternatively, the first calculation software 36 is capable of calculating intermediate images C_(n)(x,y), C_(n+m)(x,y) from two transmission images I_(n)(x,y), I_(n+m)(x,y) acquired at moments n and n+m, according to the following equation: C _(n)(x,y)=I′ _(n)(x,y)− I′ _(n)   (5) C _(n+m)(x,y)=I′ _(n+m)(x,y)− I′ _(n+m)   (6) where I′_(n)(x,y), I′_(n+m)(x,y) respectively represent a region of interest of the two transmission images I_(n) and I_(n+m). As previously stated, the index m is for example equal to 1. The coordinates x and y designate the coordinates of a point of the image, C_(n)(x,y), C_(n+m)(x,y) being matrices having N rows and M columns, and

I_(n)′, I_(m)′ representing a mean value of the respective regions of interest I_(n′)(x,y), I_(m′)(x,y). We must specify that the subtraction of I′_(n) and I′_(m) by I_(n)′ and I_(m)′, respectively, is optional. This corresponds to a normalization step, making it possible to obtain an indicator comprised between 0 and 1. Furthermore, this makes it possible to eliminate the fluctuation effect of the lighting intensity of the medium between two images.

According to this alternative, the first calculation software 36 is then capable of calculating the first indicator Ind1_(n,n+m) according to the following equation:

$\begin{matrix} {{{Ind}\; 1_{n}},_{n + m}{= \frac{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{\sqrt{C_{n}^{2}\left( {x,y} \right)}\sqrt{C_{n + m}^{2}\left( {x,y} \right)}}}{\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{C_{n}^{2}\left( {x,y} \right)}}\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{C_{n + m}^{2}\left( {x,y} \right)}}}}} & (7) \end{matrix}$

where Ind1_(n,n+m) represents the first indicator.

The characterization software 40 is capable of characterizing the variation of the speed and/or agglomeration of particles contained in the liquid 12. More specifically, the characterization software 40 is capable of determining, from the first calculated indicator Ind1_(n+m), the variation of the speed of the particles contained in the liquid 12, such as their slowing. In the embodiment described where the liquid 12 contains blood, the first characterization software 40 is capable of determining, from the first calculated indicator Ind1_(n,n+m), the coagulation of the blood particles and/or a time interval, called coagulation time, between an initial moment and the moment when the first calculated indicator Ind1_(n,n+m) takes a predetermined value. Thus, in general, Ind1_(n,n+m) characterizes a coagulation parameter of the blood, based on the observation of transmission images I_(n), I_(n+m) at moments n and n+m, m generally being comprised between 1 and 10, and preferably equal to 1.

The operation of the characterization system 10 according to the invention will now be described using FIG. 5, showing a flowchart of the characterization method according to the invention.

Before use thereof, the circulation channel(s) 28 of the fluid chamber are empty, and an initial image I₀ of the chamber 14 then shows a white area corresponding to the circulation channel 28 and areas delimiting the channel, in this example appearing in the form of dark areas corresponding to the rest of the fluid chamber 14, as shown in FIG. 6.

During the initial step 100, the liquid 12 is introduced into the deposition area 26 of the fluid chamber. The liquid 12 flows by capillarity in the deposition area 26 toward the circulation channel(s) 28.

The liquid 12 is then optionally, in step 110, mixed with a reagent 112, shown in FIGS. 3 and 4, and capable of triggering or favoring a slowing phenomenon of the particles. The reagent 112 is, for example, a lyophilized reagent capable of favoring the slowing of the blood particles through coagulation of the blood.

The reagent 112 is, for example, deposited upstream from the optical detection area corresponding to the area inside the dotted lines in FIG. 3 for which an image is acquired by the photodetector 20. Alternatively, the reagent 112 is positioned inside the optical detection area, as shown in FIG. 4. The mixing between the liquid 12 and the reagent 112 occurs when the liquid 12 flows in contact with the reagent 112 inside the circulation channel 28 (arrow F1).

In the described embodiment, the reagent 112 is a pro-coagulant protein. This protein is deposited, dried or lyophilized in the circulation channel 28. The reagent 112 is for example the prothrombin protein, also called PT, when the INR (International Normalized Ratio) parameter is being determined.

${INR} = \left( \frac{T}{Tref} \right)^{ISI}$

T being the measured coagulation time, Tref being the considered reference time, ISI being a correction factor that depends on the reagents used to trigger the coagulation.

Alternatively, the reagent 112 is the Ecarin protein, when the coagulation time is measured using the ECT (Ecarin Clotting Time) test. Alternatively, the reagent 112 is the Thrombin protein when the coagulation time is measured using the TT (Thrombin Time) test.

The liquid 12 is lighted by the light beam 18 during the step 120. The light source 16 in fact emits the light beam 18 toward the fluid chamber 14 in which the liquid 12 is found in the longitudinal direction X.

During the step 130, the matrix photodetector 20 then sequentially acquires several transmission images I_(n)(x,y), I_(n+1)(x,y) at different moments n and n+m. Each transmission image I_(n)(x,y), I_(n+m) (x,y) is formed by the transmitted radiation, and the corresponding acquisition moment, by the lighted fluid chamber 14.

The images I_(n)(x,y), I_(n+m)(x,y) are for example immediately successive images, m then being equal to 1, preferably acquired every second, as shown in FIGS. 7 and 8, where the time gap between the two images I_(n)(x,y), I_(n+1)(x,y) successively acquired is equal to 1 second.

The acquired images I_(n)(x,y), I_(n+1)(x,y) correspond to the interferences of diffraction patterns created by particles suspended in the liquid 12. The lighting of the particles by the spatially and temporally coherent beam 18, such as a laser beam, creates a diffraction pattern, which varies over time due to the movement of particles contained in the liquid 12.

The observation of a usable diffraction pattern, by placing the matrix photodetector 20 at such a small distance away, is in particular due to the absence of a magnification lens between the fluid chamber 14 and the photodetector 20.

During the acquisition step 130, the photodetector 20 is positioned at a small distance from the fluid chamber 14, the second distance D2 between the fluid chamber 14 and the photodetector 20 in the longitudinal direction X being smaller than 1 cm.

At the end of the acquisition step 130, in particular after the acquisition of the images I_(n)(x,y), I_(n+1)(x,y), the first calculation software 36 begins, in the step 140, by calculating the images A_(n)(x,y), A_(n+1)(x,y) using equations (1), (2) and (3).

The first calculation software 36 then calculates the corresponding correlation image Icorr_(n,n+1)(x,y) from the images A_(n)(x,y), A_(n+1)(x,y) and using equation (4).

In the described embodiment, the correlation images evolve as a function of time, as shown in FIGS. 9 and 10, where the correlation image I_(corrN1,N1+1)(x,y), denoted Icorr_(N1)(x,y), corresponding to correlation between two transmission images I_(n), I_(n+1) done with n=N1 before the coagulation (FIG. 9), has a very different appearance from the image Icorr_(N2)(x,y) corresponding to a correlation between two transmission images I_(N2), I_(N2+1), done with n=N2 after the coagulation (FIG. 10).

The first calculation software 36 lastly calculates the value of the first indicator Ind1_(n,n+1) for each correlation image Icorr_(n,n+1)(x,y) obtained. The value of the first indicator Ind1_(n,n+1) is for example the mean value of the points of the correlation image Icorr_(n,n+1)(x,y) in the predetermined region of interest 142, visible in FIGS. 9 and 10. Alternatively, the value of the first indicator Ind1_(n,n+1) is the integral of the image in the region of interest 142 (i.e., the sum of the gray levels of each pixel). Also alternatively, the value of the first indicator Ind1_(n,n+1) is a function of that integral.

Alternatively, the first calculation software 36 begins, in step 140, by calculating the intermediate images C_(n)(x,y), C_(n+1)(x,y) using equations (5) and (6).

The first calculation software 36 then populates the value of the first indicator Ind1_(n,n+1) from intermediate images C_(n)(x,y), C_(n+1)(x,y) and using equation (7).

In the described embodiment, at the end of the calculation step 140, the characterization method returns to step 130 in order to acquire a new image of the lighted fluid chamber 14, then to calculate, similarly to during step 140, a new correlation image Icorr_(n+1,n+2)(x,y) and a new value of the first indicator Ind1_(n+1, n+2).

The acquisition 130 and calculation 140 steps are then reiterated regularly, for example every second, for a predetermined length of time, for example longer than 60 seconds, or until a stop initiated by the user, in particular in light of the evolution over time of the first indicator.

The evolution over time of the first indicator Ind1_(n,n+m), shown in the embodiment described by the curve 145 shown in FIG. 11, then makes it possible to calculate the variation of the speed of the particles contained in the liquid 12, such as the slowing of the blood particles.

In FIG. 11, an initial moment t0 corresponds to the mixture of the reagent 112 with the liquid 12, and the circulation channel 28 across from the optical detection area is then filled at a first moment t1. In other words, the example embodiment of FIG. 11 corresponds to the case where the reagent 112 is deposited just before the optical detection area, as shown in FIG. 3.

The curve 145 then shows, from the first moment t1, a decrease in the value of the first indicator Ind1_(n,n+m) to reach a minimum value of less than 0.1. The curve 145 then shows, from the second moment t2 _(A), a rapid increase in the value of the first indicator Ind1_(n,n+m) until that value stabilizes around 0.8.

The phase between the first and second moments t1, t2 _(A), also called first phase, during which the value of the first indicator Ind1_(n,n+m) is low, corresponds to a low correlation between the transmission images successively acquired. In fact, this is due to a significant change in the diffraction pattern from one image to the other during the first phase, due to the movement of the particles suspended in a liquid 12, in the space corresponding to the region of interest 142.

The phase beginning at the second moment t2 _(A) and until the end of the characterization, also called second phase, corresponds to a swelling of the particles in a liquid 12, which amounts to an increase in the correlation between the successive images.

The characterization software 40 then determines, from the first populated indicator Ind1_(n,n+m), the time interval between the initial moment t0 and the moment t2 _(A) at which the first indicator again has increasing correlation values. The time interval between the original moment t0 and the second moment t2 _(A) is also called coagulation time Tc. In the example embodiment of FIG. 11, the coagulation time Tc is approximately equal to 29 s.

The characterization software 40 also determines the coagulation of blood particles from the first populated indicator Ind1_(n,n+m). The coagulation time T for example corresponds to the time gap between the initial moment t0 (t=0 on curve 145 in FIG. 11) and the coagulation moment, which characterizes the coagulation of blood.

This coagulation moment for example corresponds to a point where the curve 145 reaches a plateau, beyond which the values of the first indicator Ind1_(n,n+m) practically no longer evolve (second moment t2 _(A)). It will then be understood that this coagulation moment t2 _(A) can also be determined from the derivative of the time function describing the evolution of the first indicator Ind1_(n,n+m), for example when the latter drops below a certain threshold.

Alternatively, this regulation moment is determined from the second derivative of that function. From that second derivative, it is possible to situate an inflection point 146 of the curve 145. The moment t2 _(A) corresponding to that inflection point 146 is then for example used to determine the coagulation moment.

In the example embodiment of FIG. 11, a tangent 147 to the curve 145 is drawn, passing through said inflection point 146. The coagulation moment then corresponds to the abscissa at which said tangent 147 crosses the baseline of said curve (second moment t2 _(A)) or the abscissa at which said tangent 147 crosses the x-axis (third moment t2 _(B)). A baseline refers to the portion of the curve, substantially planar, preceding the sharp increase in the value of the first indicator Ind1_(n,n+m). In the illustrated example, the baseline corresponds to the portion of the curve 145 whereof the abscissa are comprised between approximately 15 seconds and 28 seconds (second moment t2 _(A)).

As previously indicated, the coagulation time T is obtained by a difference between the coagulation moment t2 _(A), t2 _(B) and the initial moment t0, chosen at the origin of the x-axis of the curve 145.

The characterization system 10 and the characterization method according to the invention therefore make it possible to characterize the variation of the speed of the particles contained in the liquid 12 over a large portion of the circulation channel(s) 28 of the fluid chamber due to the small distance between the fluid chamber 14 and the photodetector 20.

In the example embodiment of FIG. 11, the characterization system 10 and the characterization method according to the invention make it possible to characterize a slowing of the particles by detecting an increase in the correlation between the successive images.

One skilled in the art will understand that the characterization system 10 and the characterization method according to the invention make it possible, similarly, to characterize an acceleration of the particles by detecting a decrease in the correlation between the successive images.

The second distance D2 smaller than 1 cm between the fluid chamber 14 and the photodetector 20 also makes it possible to limit the bulk of the characterization system 10.

Further, the significant scope of the light beam 18 along the plane P, i.e., greater than 5 mm², and for example comprised between 5 mm² and 200 mm², makes it possible to limit the heating of the liquid 12 contained in the fluid chamber 14. In fact, the significant surface area of the light beam 18 makes it possible to have an optical density with a low power.

Furthermore, using an extended light beam and forming an image of the small distance from the chamber makes it possible to examine an even larger volume of fluid. The influence of local phenomena capable of becoming predominant when the light beam is finer is then eliminated, and the volume of analyzed fluid is practically unique. Analyzing the correlation between two transmission images I_(n), I_(n+m) makes it possible to take into account the spatial structure of the coagulation, in the plane of the microfluidic channel 28. In other words, the evolution of the coagulation of the blood is observed in two dimensions.

FIGS. 12 to 17 illustrate a second embodiment for which the elements similar to the first embodiment previously described are identified using identical references, and are not described again.

According to the second embodiment, the characterization system 10 is designed more particularly to characterize the agglomeration of particles contained in a liquid 12. The characterization system 10 is for example capable of characterizing the agglomeration of blood particles, such as red blood cells, also called agglutination of blood particles.

Information relative to the blood group is then also determined from the agglomeration state, also called agglutination state.

As known per se, the blood group can be determined using the Beth-Vincent test by detecting the presence of A or B antigens implying the absence of anti-A or anti-B antibodies. In the case where the erythrocytes of the tested blood have an A or B antigen, an antigen-antibody complex will be formed and lead to a cellular aggregation as recalled in table 200, shown in FIG. 12.

The fluid chamber 14 includes two separate circulation channels 202, 204, i.e., a first channel 202 and a second channel 204, as shown in FIG. 13.

According to the second embodiment, the light source 16 is any type of light source. The light 16 is not necessarily spatially and temporally coherent.

According to the second embodiment, the second calculation software 38 is able to calculate the second indicator Ind2 capable of characterizing the agglomeration of the particles, the second indicator Ind2 being an intensity indicator for each acquired image I_(n)(x,y). The second indicator Ind2 is representative of the histogram of the intensity of each pixel in the image I_(n), or in a region of interest thereof. It is determined for example by measuring the total intensity of the image I_(n) or in a predetermined region of interest of the image I_(n), optionally after threshold in g.

The characterization software 40 is next capable of determining an agglomeration state of the particles of the liquid 12 from the second calculated indicator Ind2. The agglomeration state is for example determined when the second indicator Ind2 exceeds a predetermined threshold.

In the described embodiment, where the liquid 12 contains blood, the particles are for example red blood cells, and the characterization software is then capable of determining information relative to the blood group from the agglomeration state.

The operation of the second embodiment will now be described using FIGS. 13 to 17.

During the initial step 100, the liquid 12, for example the blood sample of a donor whereof one wishes to determine the blood group, is introduced into the deposition area 26 of the fluid chamber. The liquid 12 then flows from the deposition area 26 toward the circulation channels 202, 204, for example by capillarity.

The liquid 12 is then mixed with first 206 and second 208 distinct reagents, during step 110, as shown in FIG. 13.

Each reagent 206, 208 is for example deposited upstream from the optical detection area corresponding to the area inside the dotted lines in FIG. 13 for which an image is acquired by the photodetector 20.

The mixture between the liquid 12 and the first and second reagents 206, 208 is done when the liquid 12 flows into contact with the first reagent 206 inside the first channel 202 (arrow F2), and respectively the second reagent 208 inside the second channel 204 (arrow F3).

In the described embodiment, the first reagent 206 is a donor A serum, i.e., containing anti-B antibodies, and the second reagent 208 is a donor B serum, i.e., containing anti-A antibodies.

Depending on the blood group associated with the blood sample 12, a cellular aggregation will or will not then occur in each of the circulation channels 202, 204.

The liquid 12, such as the blood sample mixed with the first and second reagents 206, 208, is then lighted by the light beam 18 during step 120.

During step 130, the matrix photodetector 20 then acquires a transmission image I(x,y) corresponding to an optical detection area encompassing the two circulation channels 202, 204.

One skilled in the art will observe that, according to the second embodiment, the acquisition of a single transmission image I(x,y) makes it possible to characterize the agglomeration of the particles contained in the liquid 12, by comparing that image with a reference image I_(ref)(x,y), the latter for example being an image made over a reference area, not shown, in which the blood is not mixed with the reagent. This reference area is for example a third channel, with a geometry identical to that of the first or second channel 202, 204, and not including any reagent.

Alternatively, this is an area situated on the first channel 202 or on the second channel 204, upstream from the reagent 206, 208.

Alternatively, the reference image I_(ref)(x,y) is produced at the same location as the transmission image, just after filling of the channel by the analyzed liquid, the transmission image I(x,y) being done, under the same conditions, after certain time, for example 1 minute, such that any effect of the reagent on the analyzed liquid is measurable.

The acquired image I(x,y) corresponds similarly to the diffraction and the diffusion of the light beam 18 by the particles suspended in a liquid 12. Preferably, this image is done under identical conditions for the two channels, as well as for the reference area. For identical conditions, we particularly refer to the lighting conditions, the source-detector distance, the characteristics of the detector used, the placement time, the observed field, the size of the image.

The lighting of the particles by the light beam 18 creates a diffraction pattern. As previously indicated, the absence of a magnification lens between the fluid and the photodetector 20, coupled with the significant surface of the incident beam, makes it possible to form a usable image at a short distance, covering a large fluid field, such as a field having an area of several millimeters squared.

During the acquisition step 130, the photodetector 20 is placed near the fluid chamber 14, the second distance D2 between the fluid chamber 14 and the photodetector 20 in the longitudinal direction X being smaller than 1 cm.

In the example embodiment of FIG. 14, showing an acquired image 210 of the first channel 202, and respectively an acquired image 212 of the second channel 204, the cellular aggregation is observed only in the second channel 204 through the presence of white stains in the image 212. In other words, the blood group associated with the tested blood sample is group B according to the table 200 of FIG. 12.

At the end of the acquisition step 130, the second calculation software 38 calculates, during step 140, the second indicator Ind2 able to characterize the agglomeration of the particles, the second indicator Ind2 being an intensity indicator for each acquired image I(x,y). The second indicator Ind2 is representative of the intensity in the predetermined region of interest of the image, in particular the distribution of the intensity of the pixels in said region.

The second indicator Ind2 is, for example, a characteristic of the image, and in particular of histogram of the gray level of the image acquired for each channel 202, 204, and, if applicable, the reference channel as illustrated in FIGS. 16 and 17, showing a histogram 214 of the gray level of the acquired image of the first channel 202, and respectively a histogram 216 of the gray level of the acquired image of the second channel 204. Each gray level histogram 214, 216 has the gray level values on the x-axis and the pixel population on the y-axis, i.e., the number of pixels, for a gray level given on the x-axis. A characteristic of the histogram is for example the mean intensity of the pixels, denoted I_(mean), after thresholding of the image, that thresholding making it possible to keep only the information for the pixels whereof the intensity is above a certain threshold.

Referring to the example shown in FIGS. 16 and 17, after having performed thresholding at the intensity corresponding to the value 120, it is understood that the mean intensity of the image corresponding to FIG. 17 is higher than the mean intensity of the image corresponding to FIG. 16. This is due to the fact that the histogram of FIG. 17, showing the observation of an agglutination of particles, comprises more intense pixels (gray levels higher than 200) than the histogram of FIG. 16, the latter showing the observation of a non-agglutination. The second indicator Ind2 is then for example established according to the mean intensity of the image.

According to one alternative, one determines, for each produced image, the intensity I_(max), the latter corresponding, on the histogram of the image, to the highest value of the intensity bringing together a predetermined number of pixels, for example 500 pixels. One then determines the deviation between I_(max) and I_(mean), by subtracting I_(max)−I_(mean), the second indicator Ind2 then representing that deviation. On the histogram of FIG. 17, the second indicator Ind2 thus defined is higher than in the histogram of FIG. 16. The value of the second indicator Ind2 thus determined makes it possible to conclude on the presence or absence of an agglutination phenomenon through a comparison, for example with a value Ind2_(ref) obtained on a reference area, or with a predetermined value, the predetermination of that value for example being done according to experimental tests.

According to one alternative, on each transmission image, the intensity I_(peak) corresponding to the maximum value of the histogram is determined, i.e., the intensity value bringing together the highest number of pixels. In FIGS. 16 and 17, this value corresponds to the peak of each distribution, respectively equal to 120 and 130. One also determines the maximum value I_(max) bringing together a number of pixels with a value above a predetermined threshold. In reference to FIGS. 16 and 17, and by adopting a threshold of 500, I_(max) is respectively equal to 181 and 256. The second indicator Ind2 corresponds to the distance between I_(max) and I_(peak), 61 for FIGS. 16 and 126 for FIG. 17, respectively. One concludes on an agglutination when the second indicator Ind2 is higher, for example by 25%, than a certain predetermined threshold, or when it is higher than the indicator Ind2_(ref) established for the reference area.

According to one alternative, the second indicator Ind2 is a comparison indicator between a region of interest of the transmission image I and a reference image I_(ref) not containing reagent (and therefore in which the agglutination does not occur), as below:

${{Ind}\; 2} = \frac{\sum\limits_{x}^{\;}\;{\sum\limits_{y}^{\;}\;{{{I\left( {x,y} \right)} - {I_{ref}\left( {x,y} \right)}}}}}{{\sum\limits_{x}^{\;}\;{\sum\limits_{y}^{\;}{I\left( {x,y} \right)}}} + {I_{ref}\left( {x,y} \right)}}$

The second indicator Ind2 is compared with a predetermined threshold, for example 0.25. Thus, if the second indicator Ind2 is above that threshold, an agglutination is found.

The characterization software 40 is capable of then determining an agglomeration state of the particles of the liquid 12 from the second calculated indicator Ind2.

The agglomeration state is for example determined when the second indicator Ind2 exceeds a predetermined threshold.

If the comparison is positive, i.e., if the gray level obtained is greater than the predetermined threshold, then the characterization software 40 deduces the presence of a cellular aggregation in the corresponding channel 202, 204.

In the second described embodiment, the characterization software 40 lastly determines the blood group associated with the blood sample 12 tested from the type of the first and second regions 206, 208, as well as from the table 200.

The advantages of this second embodiment are identical to those of the first embodiment previously described.

As a complement to the first embodiment, the fluid chamber 14 includes a plurality of channels, for example the two channels 202, 204 visible in FIG. 13, so as to characterize the variation of the speed of the particles of the liquid 12 when the liquid 12 is mixed with different reagents, a respective reagent then being positioned in each channel 202, 204 of the fluid chamber 14. This makes it possible, with a same device, to determine different analysis parameters of the same liquid sample, for example the coagulation time and the blood group.

Such a fluid chamber 14 is for example advantageous to characterize the coagulation of the liquid 12 containing blood, with different reagents capable of favoring the slowing of the blood particles via a coagulation of the blood, such as the different reagents 112 defined above.

One can see that the characterization system 10 according to the invention makes it possible to observe a larger part of the fluid chamber 14, while having a limited bulk.

FIGS. 18 to 29 illustrate a second example of the second embodiment, in which the liquid to be characterized 12 is a biological liquid, in particular blood or diluted blood, and the characterization system 10 is capable of characterizing the agglomeration of particles, in this case red blood cells, in the biological liquid 12.

The liquid to be characterized 12 in this example includes blood diluted at 1/20 in a PBS (Phosphate Buffered Saline) buffer, the buffer including 1% by volume of FBS (Fetal Bovine Serum).

The volume of diluted blood is 40 μL, to which a variable quantity of antibodies is added, such as an anti-red blood cell called CD235A, for example marketed by the company Becton Dickinson under reference BD 555569. The quantity of antibodies added varies from 0 to 1 μg of antibodies per μl of undiluted blood, which corresponds to a concentration comprised between 0 and 6.7 μM.

The addition of these antibodies makes it possible to mask the surface antigens of the red blood cells (in particular glycophorin A), which causes their agglutination.

The aim of this second example is to show that it is possible to characterize an agglutination state of blood particles, for example red blood cells, by lenseless imaging using the characterization system 10.

For each quantity of antibodies added into the liquid to be characterized 12, the liquid sample to be characterized 12 is acquired using the characterization system 10, i.e., by lenseless imaging, the obtained images 220A, 220B, 220C and 220D being visible in FIGS. 18 to 21. A grayscale histogram of the intensity of the pixels of each of these images 220A, 220B, 220C and 220D is then calculated, the calculated histograms 222A, 222B, 222C and 222D being visible in FIGS. 22 to 25. Reference images 224A, 224B, 224C and 224D of the liquid sample to be characterized 12 are also obtained with a microscope, as shown in FIGS. 26 to 29. It should be noted that, for microscopic observation, the blood sample is diluted with a dilution factor of 1/10.

In the second example, the light source 16 is a laser diode, having an emission spectrum centered on a wavelength λ for example equal to 670 nm, and the first distance D1 is substantially equal to 8 cm. The sample is confined in the fluid chamber 14 including a channel 28 with a thickness of 150 μm formed between two transparent walls with a thickness of 200 μm. These walls are made from a plastic material, for example from COP (Cyclo Olefin Polymer).

The fluid chamber 14 is directly placed on the glass cover of the matrix photodetector 20, such as a CMOS sensor, including 1280*1024 pixels, each pixel having size 5 μm×5 μm, such that the fluid chamber 14 is positioned between the CMOS sensor and the light source 16. The second distance D2 is then preferably smaller than 1 cm, for example equal to 550 μm.

The image acquisitions are for example done with an exposure time of 5 ms, with one image per acquisition. The images 220A, 220B, 220C and 220D respectively correspond to an increasing added quantity of antibodies. More specifically, the images 220A, 220B, 220C and 220D respectively correspond to:

-   -   a substantially zero quantity of antibodies,     -   a quantity of antibodies below a threshold concentration C,     -   a quantity of antibodies equal to the threshold concentration C,         and     -   a quantity of antibodies equal to 2 times the threshold         concentration C.

When the quantity of added antibodies exceeds the threshold concentration C, the red blood cells agglomerate and the images obtained by lenseless imaging using the characterization system 10 reflect the size of the agglutinates. The value of the threshold concentration C is for example equal to 250 ng of antibodies for 1 μl of undiluted blood, which corresponds to 1.7 μM.

It will be observed that the agglomeration of red blood cells causes the appearance of extended light areas (high gray level) delimited by dark areas (low gray level). This image segmentation effect into areas including several tens, or hundreds of pixels, of comparable gray levels, may be observed by comparing the images 220A (FIG. 18) or 220B (FIG. 19), in which no agglutination is observed, with the images 220C (FIG. 20) and 220D (FIG. 21), in which agglutination is observed. The presence or absence of an agglutination of particles observable in the images 220A, 220B, 220C and 220D is confirmed by the microscope observations shown in the images 224A (FIG. 26), 224B (FIG. 27), 224C (FIG. 28) and 224D (FIG. 29), respectively. This results in an evolution of the histogram of each image, the latter tending to stretch toward the low gray level values as the quantity of agglomerates increases, as shown from the histogram 222A corresponding to the image 220A toward the histogram 222D corresponding to the image 220D.

The agglutination state of the blood sample is then quantified by calculating the second indicator Ind2 according to several possible alternatives:

-   -   according to a first alternative, the second indicator Ind2 is         equal to the standard deviation of the intensity distribution of         the pixels of the examined area of interest, and is then denoted         Ind2_(A),     -   according to a second alternative, the second indicator Ind2 is         equal to the number of pixels below a certain threshold, that         threshold for example being a fraction of the maximum gray         level, divided by the total number of pixels in the examined         area of interest, and the second indicator Ind2 calculated         according to this second alternative is then denoted Ind2_(B).         In the example of FIGS. 22 to 25, the threshold value is equal         to 125.

Table 1 below shows the value of the second indicator Ind2 according to these two alternatives and for each of the areas of interest shown in FIGS. 18 to 21.

FIGS. 18 19 20 21 Ind2_(A) 36 33 51 59 Ind2_(B) 6.5 10⁻³ 6.5 10⁻³ 8.7 10⁻² 1.4 10⁻¹

When the second indicator Ind2_(A) according to the first alternative is below a threshold value, for example comprised between 40 and 45, there is no observable agglutination. Beyond that threshold value, the higher the value of the second indicator Ind2_(A), the greater the quantity of agglutinated particles.

The second indicator Ind2_(B), calculated according to the second alternative, makes it possible to reach the same conclusions, taking a threshold value comprised between 1 10⁻² and 5 10⁻²

One can see that it is possible to observe, or even quantify, an agglutination state of particles in the biological liquid 12, using an indicator calculated from an image obtained by the characterization system 10, i.e., by lenseless imaging, and in particular the second indicator Ind2_(A), Ind2_(B), according to the first and second alternatives of this example, said second indicator depending on the distribution of the intensity of the pixels of the images 220A, 220B, 220C and 220D acquired by the characterization system 10.

The characterization system 10 can also be used in a diagnostic test based on the detection of agglutinates in a biological fluid.

FIGS. 30 to 45 illustrate a third example of the second embodiment, in which the liquid to be characterized 12 is a biological liquid, in particular blood or diluted blood, and the characterization system 10 is capable of characterizing the agglomeration, also called agglutination, of particles in the biological liquid 12.

In this third example, the detection of the agglutination of red blood cells in a blood sample is shown, including a variable quantity of A protein, the agglutination being caused by the addition of a given quantity of a reagent (an antibody).

The liquid to be characterized 12 for example includes blood diluted at 1/20 in a PBS (Phosphate Buffered Saline) buffer, the buffer including 1% by volume of FBS (Fetal Bovine Serum).

The volume of diluted blood is 40 μL, to which an antibody is incubated, such as an anti-red blood cell called CD235A, for example marketed by the company Becton Dickinson under reference BD 555569, with an A protein solution in a variable quantity. The incubation duration is 1 hour.

Thus, there are several so-called antibody—A protein solutions available, in which the antibody—A protein molar ratio is variable. The solutions may cause the agglutination of red blood cells, resulting in the name “pro-equipment solutions”. A volume of 1.2 μL of each of these solutions is incubated with 40 μL of diluted blood sample described above, for 1.5 hours, each of these mixtures forming a liquid sample to be characterized 12.

In each of the mixtures thus obtained, the S antibody molar concentration is below the threshold C determined in the previous second example. In other words, this antibody concentration does not allow the spontaneous agglutination of red blood cells. In the case at hand, this concentration S is 100 ng of antibodies per μl of undiluted blood, i.e., 0.7 μM.

For each of the liquid samples to be characterized 12, an image acquisition is done using the characterization system 10, i.e., by lenseless imaging, the acquired images 230A, 230B, 230C, 230D and 230E obtained being shown in FIGS. 30 to 34. A grayscale histogram of the intensity of the pixels of each of these images 230A, 230B, 230C, 230D and 230E is then calculated, the calculated histograms 232A, 232B, 232C, 232D and 232E being shown in FIGS. 35 to 39. Reference images 234A, 234B, 234C, 234D and 234E of each of the liquid samples to be characterized 12 are also obtained with a microscope, as shown in FIGS. 40 to 44. It should be noted that, for microscopic observation, the blood sample is diluted with a dilution factor of 1/10.

In this third example, the light source 16 is a laser diode, having an emission spectrum centered on a wavelength λ equal to 670 nm, and the first distance D1 is substantially equal to 8 cm. The sample is confined in the fluid chamber 14 including a channel 28 with a thickness of 150 μm formed between two transparent walls with a thickness of 200 μm. These walls are made from a plastic material, for example COP (Cyclo Olefin Polymer).

The fluid chamber 14 is directly placed on the glass cover of the matrix photodetector 20, such as a CMOS sensor. The CMOS sensor for example has a matrix of 1280 by 1024 pixels, each pixel being in the shape of a square, each side measuring 5 μm, such that the fluid chamber 14 is positioned between the CMOS sensor and the light source 16. The second distance D2 is then preferably smaller than 1 cm, for example equal to 550 μm.

The image acquisitions are for example done with an exposure time of 5 ms, with one image per acquisition. The images 230A, 230B, 230C, 230D and 230E respectively correspond to an increasing added quantity of A protein. More specifically, the images 230A, 230B, 230C, 230D and 230E respectively correspond to:

-   -   absence of antibodies, i.e., an Antibody:A Protein molar         ratio=0:40 (0 antibody molecules for 40 A protein molecules),     -   absence of A protein, i.e., an Antibody—A Protein molar         ratio=1:0 (1 antibody molecule for 0 A protein molecules),     -   Antibody:A Protein molar ratio=1:1 (1 antibody molecule for 1 A         protein molecule),     -   Antibody:A Protein molar ratio=1:5 (1 antibody molecule for 5 A         protein molecules), and     -   Antibody:A Protein molar ratio=1:40 (1 antibody molecule for 40         A protein molecules).

The antibody here serves as a bonding agent between an A protein molecule and a red blood cell, as will be outlined later.

In the presence of A protein and in the absence of antibodies, no red blood cell agglutination is observed, as shown in FIG. 30. In the presence of antibodies and the absence of A protein, no red blood cell agglutination is observed, which is shown in FIG. 31.

When the antibody:A protein ratio is equal to 1:1, there is also no observed red blood cell agglutination, as shown in FIG. 32.

When the antibody:A protein ratio is equal to 1:5, red blood cell agglutination is observed, shown in FIG. 33. When the antibody:A protein ratio is equal to 1:40, red blood cell agglutination is also observed, shown in FIG. 34, the size of the agglutinates observed in FIG. 34 being larger than that of the agglutinates observed in FIG. 33.

FIGS. 35 to 39 show the histogram of the intensity of the pixels of FIGS. 30 to 34, respectively.

One can see that the agglomeration of red blood cells causes the appearance of light areas (high gray level) delimited by a dark area (low gray level). This image segmentation effect into areas including several tens, or hundreds of pixels, of comparable gray levels, may be observed by comparing the images 230A (FIG. 30) or 230B (FIG. 31) or 230C (FIG. 32), in which no agglutination is observed, with images 230D (FIG. 33) and 230E (FIG. 34), in which agglutination is observed. The presence or absence of an agglutination of particles observable in the images 230A, 230B, 230C, 230D and 230E is confirmed by the microscope observations shown in the images 234A (FIG. 40), 234B (FIG. 41), 234C (FIG. 42), 234D (FIG. 43) and 234E (FIG. 44), respectively. This results in an evolution of the histogram of each image, the latter tending to stretch toward the low gray level values as the quantity of agglomerates increases, as shown from the histogram 232A corresponding to the image 230A toward the histogram 232E corresponding to the image 230E. The agglutination state of the blood sample is then quantified by calculating the second indicator Ind2 according to several possible alternatives:

-   -   according to a first alternative, the second indicator Ind2 is         equal to the standard deviation of the intensity distribution of         the pixels of the examined area of interest, and is then denoted         Ind2_(A),     -   according to a second alternative, the second indicator Ind2 is         equal to the number of pixels below a certain threshold, that         threshold for example being a fraction of the maximum gray         level, divided by the total number of pixels in the examined         area of interest, and the second indicator Ind2 calculated         according to this second alternative is then denoted Ind2_(B).         In the example of FIGS. 22 to 25, the threshold value is equal         to 125.

Table 2 below shows the value of the second indicator Ind2 according to these two alternatives and for each of the areas of interest shown in FIGS. 30 to 34.

FIGS. 30 31 32 33 34 Ind2_(A) 33 39 35 50 52 Ind2_(B) 4.0 10⁻³ 1.5 10⁻² 5.5 10⁻³ 6.5 10⁻² 9.5 10⁻²

When the second indicator Ind2_(A) according to the first alternative is below a threshold value, for example comprised between 40 and 45, there is no observable agglutination. Beyond that threshold value, the higher the value of the second indicator Ind2_(A), the greater the quantity of agglutinated particles.

The second indicator Ind2_(B), calculated according to the second alternative, makes it possible to arrive at the same conclusions, using a threshold value comprised between 1 10⁻² and 5 10⁻².

One can thus see that it is possible to observe, or even quantify, an agglutination state of particles in the biological liquid 12, using an indicator calculated from an image obtained by the characterization system 10, i.e., by lenseless imaging, and in particular the second indicator Ind2_(A), Ind2_(B), according to the first and second alternatives of this example, which depends on the distribution of the intensity of the pixels of the images 230A, 230B, 230C, 230D and 230E acquired by the characterization system 10.

Furthermore, the greater the quantity of A protein, the larger the size of the agglutinates, the quantity of antibodies added being constant. Thus, the second indicator Ind2_(A), Ind2_(B) quantifying the agglutination state may also quantify a quantity of protein in the blood sample.

Depending on the antibody-A protein molar ratio, the red blood cells agglomerate and the images 230A, 230B, 230C, 230D and 230E obtained by lenseless imaging reflect the size of the agglutinates, i.e., the degree of agglutination. It is then understood that by introducing a predetermined quantity of antibodies into the blood sample, it is possible to estimate the quantity of A protein present in that sample according to the agglutination state, i.e., according to the second indicator Ind2_(A), Ind2_(B) previously described.

In other words, the quantity of A protein beyond which agglutination is observed constitutes the detection limit for assaying that protein in a blood sample, by introducing a given quantity of antibodies into the liquid sample to be characterized 12.

Thus, one will understand that it is possible to observe, or even quantify, an agglutination state of particles in a biological fluid, using indicators relative to the image obtained by lenseless imaging, and in particular the second indicator Ind2_(A), Ind2_(B), according to the first and second alternatives of this second example, which depends on the distribution of the intensity of the pixels. This agglutination state for example depends on the concentration of an analyte in the biological liquid, the quantification of that agglutination state then making it possible to assay that analyte in the liquid. The example shows that that assay can be done by introducing a bifunctional reagent, in this case the antibody 300, into a blood sample, capable of bonding both on a particle of the biological fluid, in this case the red blood cells 302, and on the analyte to be assayed, in this case the A protein 304, thereby forming a bridge between an analyte 304 and the red blood cells 302, as shown in FIG. 45.

The term bifunctional designates the ability of the reagent to bond both on a particle and an analyte.

In general, the term “analyte” refers to a chemical or biological species present in the liquid, such as a molecule, a macromolecule (for example, protein or nucleic acid), a cell, a bacteria, a virus, or spore.

Furthermore, the analyte 304 must include at least two bonding sites with the bifunctional reagent, as shown in FIG. 45. Thus, each analyte 304 may be bonded, via the bifunctional reagent, to at least two particles. This causes agglutination of the particles.

In other words, the agglutination state of the particles in the liquid 12 depends on the quantity of analyte present in the liquid 12, that quantity being able to be assayed by adding a reagent capable of causing the formation of agglutinates, the reagent 300 then being able to bond between one of said particles 302 and an analyte 304 so as to form an agglutinate.

As a function of the quantity of analyte 304 present in the liquid, an agglutinate is formed, made up of particles 302 and analytes 304. By determining the agglutination state corresponding to a given quantity of introduced reagent, it is then possible to estimate the quantity of analyte 304 present in the liquid 12.

In the second and third examples of the second embodiment, previously described, the second distance D2 is smaller than 1 cm. The inventors nevertheless also observed that, in the case of the characterization of agglutination, values of the second distance D2 greater than 1 cm, such as values of several centimeters, or even several tens of centimeters, do make it possible to obtain usable results, although values of the second distance D2 below 1 cm remain preferable.

In general, these second and third examples demonstrate another aspect of the invention. According to this other aspect, the invention relates to a method for characterizing the agglutination of particles, such as biological particles, in a liquid, for example a biological liquid, and in particular a bodily fluid, the characterization method including the following steps:

-   -   introducing liquid into a fluid chamber,     -   lighting the fluid chamber using a light beam, the light beam in         particular coming from a light source, such as a laser diode or         a light-emitting diode,     -   acquiring an image, or a plurality of images, of the fluid         chamber using a matrix photodetector, the photodetector         preferably being placed at a distance from the fluid chamber of         less than 1 cm, the fluid chamber being positioned between the         light source and the matrix photodetector.     -   processing the image, or the plurality of images, to determine         an indicator characterizing the agglutination of particles in         the biological liquid, and     -   characterizing the agglutination of particles in the liquid,         depending on the value of the indicator.

It should be noted that the image is acquired by the photodetector, preferably without a magnifying lens between the fluid chamber and the matrix photodetector. However, objective microlenses may be provided at each pixel of the detector, as previously stated.

Additionally and optionally, the indicator is an indicator representing the distribution of the intensity of the pixels in an image, or more generally, any other indicator translating the segmentation of the image into different areas, each area including several tens to hundreds of pixels of comparable intensity, i.e., where the intensity is distributed in a range of gray levels of approximately half, or even one third, or even one quarter, or even less than one quarter of the dynamic of the image.

Additionally and optionally, the characterization method includes the addition of a reagent, which can cause the agglutination of particles in the liquid.

As illustrated in the third example of the second embodiment, agglutination of the particles for example depends on a quantity of analyte present in the liquid.

According to this alternative, the invention relates to a method for detecting the quantity of an analyte in a liquid, for example a biological liquid, and in particular a bodily fluid, the detection method including the following steps:

-   -   introducing liquid into a fluid chamber,     -   lighting the fluid chamber using a light beam, the light beam in         particular coming from a light source, such as a laser diode or         a light-emitting diode,     -   adding a reagent, capable of causing the formation of         agglutinates of particles and analytes in the liquid,     -   acquiring an image, or a plurality of images, of the fluid         chamber using a matrix photodetector, the photodetector         preferably being placed at a distance from the fluid chamber of         less than 1 cm, the fluid chamber being positioned between the         light source and the matrix photodetector,     -   processing the image, or the plurality of images, to determine         an indicator characterizing the agglutination of particles in         the biological liquid, and     -   estimating the quantity of analyte in the liquid, as a function         of the value of the indicator.

According to still another aspect, the invention relates to a method for determining a parameter of the liquid 12, including blood, the method including the following steps:

-   -   introducing the liquid 12 in the fluid chamber 14,     -   lighting the fluid chamber 14 using the excitation light beam 18         emitted by the light source 16, the light beam 18 extending         through the fluid chamber 14 in the longitudinal direction x,     -   acquiring at least one image I_(n)(x,y), I_(n+m)(x,y), I(x,y)         using the matrix photodetector 20, the image I_(n)(x,y),         I_(n+m)(x,y), I(x,y) being formed by radiation transmitted by         the lighted fluid chamber 14, and     -   determining an indicator Ind1_(n,n+m), Ind2, from said at least         one image I_(n)(x,y), I_(n+m)(x,y), I(x,y).

During the acquisition step, the photodetector 20 is positioned at the distance D2, smaller than 1 cm, from the fluid chamber 14 in the longitudinal direction X.

As a complement and optionally, the determination method comprises one or more of the following features, considered alone or according to all technically possible combinations:

-   -   the light beam 18 directly lights the fluid chamber 14, and the         image I_(n)(x,y), I_(n+m)(x,y), I(x,y) is formed directly by the         radiation transmitted by the lighted fluid chamber 14, in the         absence of a magnification lens positioned between the fluid         chamber 14 and the photodetector 20;     -   the parameter is a coagulation, and the method then includes the         following steps:         -   mixing the liquid 12 with a reagent to favor the coagulation             of the blood,         -   acquiring a series of transmission images I_(n)(x,y),             I_(n+m)(x,y) at different moments n, n+m,         -   calculating an indicator Ind1_(n,n+m) to establish a             correlation between two areas of the transmission images             I_(n)(x,y), I_(n+m)(x,y),

the coagulation being determined as a function of the value of said indicator;

-   -   the parameter is a coagulation time, and the method then         includes the following steps:         -   mixing the liquid 12 with a reagent to favor the coagulation             of the blood,         -   acquiring a series of transmission images I_(n)(x,y),             I_(n+m)(x,y) at different moments n, n+m,         -   calculating an indicator Ind1_(n,n+m) to establish a             correlation between two images I_(n)(x,y), I_(n+m)(x,y), and         -   determining a time interval, called coagulation time,             between an initial moment t0 and the moment t2 _(A), t2 _(B)             at which the indicator Ind1_(n,n+m) takes a predetermined             value.     -   The parameter is an agglutination of blood particles, and the         method then includes the following steps:         -   mixing the liquid 12 with a reagent capable of creating an             agglutination of the blood particles,         -   acquiring a transmission image I(x,y),         -   calculating an indicator Ind2 as a function of the intensity             in a predetermined area of the transmission image I(x,y),             and         -   determining an agglutination state when that indicator Ind2             exceeds a predetermined threshold;     -   the blood particles are red blood cells, the reagent including         an antibody, the agglutination state then providing information         relative to the blood group.

According to this other independent aspect, the invention also relates to a system for determining a parameter of the liquid 12, including blood, the determination system comprising:

-   -   the fluid chamber 14 designed to receive the liquid 12;     -   the light source 16 capable of emitting the excitation light         beam 18 to light the fluid chamber 14, the light beam 18         extending the longitudinal direction X;     -   the matrix photodetector 20 capable of acquiring at least one         image I_(n)(x,y), I_(n+1)(x,y), I(x,y) of a radiation         transmitted by the lighted fluid chamber 14; and     -   the information processing unit 21 including means for         determining an indicator Ind1_(n,n+m), Ind2, from said at least         one image I_(n)(x,y), I_(n+m)(x,y), I(x,y).

The photodetector 20 is positioned at the distance D2, smaller than 1 cm, from the fluid chamber 14 in the longitudinal direction X.

The parameter is a coagulation, coagulation time, or an agglutination of blood particles. 

The invention claimed is:
 1. A method for characterizing a variation in the speed of particles, the particles being contained in a liquid, the liquid including blood wherein the method comprises: introducing the liquid into a fluid chamber, said fluid chamber having a thickness ranging from 20 μm to 1000 μm; lighting the fluid chamber using an excitation light beam emitted by a spatially coherent light source, the light beam extending through the fluid chamber in a longitudinal direction (X); acquiring at least two images (I_(n), I_(n+m)), respectively at times n and n+m, each image including multiple points (I_(n)(x,y), I_(n+m)(x,y)), wherein x,y represents the coordinates of each point in the image using a matrix photodetector, the images (I_(n), I_(n+m)) being formed by radiation transmitted by the lighted fluid chamber; and calculating, from said at least two acquired images (I_(n), I_(n+m)), a plurality of indicators (Ind1_(n,n+m)) characterizing the variation of the speed of the particles; wherein, during the acquisition step, the photodetector is positioned at a distance smaller than 1 cm from the fluid chamber in the longitudinal direction, and during the calculation step, each calculated indicator is a correlation indicator (Ind1_(n,n+m)) representing a spatial correlation between said two images (I_(n), I_(n+m)), so as to characterize the variation of speed of particles, and wherein the method further comprises deriving a blood coagulation time from said plurality of correlation indicators (Ind1_(n,n+m)).
 2. The method according to claim 1, wherein the light beam has a surface area comprised between 5 mm² and 200 mm²in a plane perpendicular to the longitudinal direction, said plane being arranged in contact with the fluid chamber.
 3. The method according to claim 1, wherein the light beam directly lights the fluid chamber, and the images (I_(n)(x,y), I_(n+m)(x,y)) are formed directly by the radiation transmitted by the lighted fluid chamber, in the absence of a magnifying lens positioned between the fluid chamber and the photodetector.
 4. The method according to claim 1, wherein the method also includes a step for mixing the liquid with a reagent capable of slowing the particles.
 5. The method according to claim 4, wherein the reagent is capable of slowing the particles by means of coagulation of the blood.
 6. The method according to claim 1, wherein the spatially coherent light source is a spatially and temporally coherent light source, such as a laser.
 7. The method according to claim 1, wherein the correlations indicators (Ind1_(n,n+m)) are calculated from a correlation image (Icorr_(n,n+m)) defining the spatial correlation between said transmission images (I_(n), I_(n+m)), or from a predetermined region of said correlation image (Icorr_(n,n+m)).
 8. The method according to claim 7, wherein the correlation image (Icorr_(n,n+m)) is determined by the following equation: (Icorr_(n,n+m)) where x and y represent the coordinates of multiple points of the image, (Icorr_(n,n+m)) is a matrix having X rows and Y columns, k1(x,y) represents a predetermined matrix having P rows and Q columns, A_(n)(x,y) and A_(n+m)(x,y) are defined by the following equations: A _(n)(x,y)=I _(n)(x,y)−(I _(n) {circle around (x)}k1)(x,y) A _(n+m)(x,y)=I _(n+m)(x,y)−(I _(n+m) {circle around (x)}k1)(x,y) I_(n)(x,y), I_(n+m)(x,y) representing two successive transmission images at moments n and n+m, I_(n)(x,y), I_(n+m)(x,y) being matrices with X rows and Y columns, and the symbol {circle around (x)} represents the convolution defined by: ${\left( {{F \otimes k}\; 1} \right)\left( {x,y} \right)} = {\sum\limits_{p = 0}^{P}\;{\sum\limits_{q = 0}^{Q}\;{{F\left( {{x - p},{y - q}} \right)}k\; 1\left( {p,q} \right)}}}$ F(x,y) being a matrix with X rows and Y columns, X, Y, P and Q being integers verifying X≧P≧1 and Y≧Q≧1.
 9. The method according to claim 1, wherein the correlation indicators (Ind1_(n,n+m)) are calculated using the following equation: ${{Ind}\; 1_{n}},_{n + m}{= \frac{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{\sqrt{C_{n}^{2}\left( {x,y} \right)}\sqrt{C_{n + m}^{2}\left( {x,y} \right)}}}{\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{C_{n}^{2}\left( {x,y} \right)}}\sqrt{\sum\limits_{{x = 1},{y = 1}}^{N,M}\;{C_{n + m}^{2}\left( {x,y} \right)}}}}$ where Ind1_(n,n+m) represents the first indicator, C_(n)(x,y) and C_(n+m)(x,y) are defined by the following equations: C _(n)(x,y)=I′ _(n)(x,y)− I′ _(n) C _(n+m)(x,y)=I′ _(n+m)(x,y)− I′ _(n+m) I′_(n)(x,y), I′_(n+m)(x,y) respectively represent a predetermined region of two successive transmission images at moments n and n+m, x and y designating the coordinates of a point of the image, I′_(n)(x,y), I′_(n+m)(x,y) being matrices having N rows and M columns, and I′_(n) , I′_(n+m) representing a mean value of respective predetermined regions I′_(n)(x,y) and I′_(n+m)(x,y).
 10. The method according to claim 1, wherein the fluid chamber includes several fluid circulation channels, and wherein, during the calculation step, an indicator (Ind1_(n,n+m)) is calculated for each of the channels. 